In this work, we showed that this year's ACIC Data Challenge admitted a decomposition similar to the one used by [1]}'s Bayesian casual forest model.
Because the bcf package could not scale to the size of the data in the competition, we implemented a faster and less resource-intensive version, which is available in the flexBCF package.
We investigated the sensitivity of our competition submission to two modeling choices - (i) the choice of propensity score method, and (ii) enforcing (or not) sparsity in the BART priors, for both practice-level and beneficiary-level modeling.
We found that the beneficiary-level modeling did not give any more accurate results than the practice level modeling.
Although we did not observe much sensitivity to our modeling choices on the Challenge datasets, our re-analysis does suggest that fitting BCF-like models with flexible propensity score estimates may yield slightly longer uncertainty intervals with better frequentist coverage.
